AIMC Topic: Humans

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Novel radiomic features versus deep learning: differentiating brain metastases from pathological lung cancer types in small datasets.

The British journal of radiology
OBJECTIVE: Accurate diagnosis and early treatment are crucial for survival in patients with brain metastases. This study aims to expand the capability of radiomics-based classification algorithms with novel features and compare results with deep lear...

On the Early and Affordable Diagnosis of Joint Pathologies Using Acoustic Emissions, Deep Learning Decompositions and Prediction Machines.

Sensors (Basel, Switzerland)
The condition of a joint in a human being is prone to wear and several pathologies, particularly in the elderly and athletes. Current means towards assessing the overall condition of a joint to assess for a pathology involve using tools such as X-ray...

A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation.

Journal of neuroengineering and rehabilitation
BACKGROUND: Markerless motion capture (MMC) technology has been developed to avoid the need for body marker placement during motion tracking and analysis of human movement. Although researchers have long proposed the use of MMC technology in clinical...

Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model.

Critical care (London, England)
BACKGROUND: Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standar...

Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data.

Scientific reports
Preoperative risk assessment is essential for shared decision-making and adequate perioperative care. Common scores provide limited predictive quality and lack personalized information. The aim of this study was to create an interpretable machine-lea...

The Gap Between AI and Bedside: Participatory Workshop on the Barriers to the Integration, Translation, and Adoption of Digital Health Care and AI Startup Technology Into Clinical Practice.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) and digital health technological innovations from startup companies used in clinical practice can yield better health outcomes, reduce health care costs, and improve patients' experience. However, the integrat...

Drainless robot-assisted minimally invasive oesophagectomy-randomized controlled trial (RESPECT).

Trials
BACKGROUND: The purpose of this randomized trial is to evaluate the early removal of postoperative drains after robot-assisted minimally invasive oesophagectomy (RAMIE). Evidence is lacking about feasibility, associated pain, recovery, and morbidity.

Collimation border with U-Net segmentation on chest radiographs compared to radiologists.

Radiography (London, England : 1995)
INTRODUCTION: Chest Radiography (CXR) is a common radiographic procedure. Radiation exposure to patients should be kept as low as reasonably achievable (ALARA), and monitored continuously as part of quality assurance (QA) programs. One of the most ef...

Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance.

Radiology
Background Automation bias (the propensity for humans to favor suggestions from automated decision-making systems) is a known source of error in human-machine interactions, but its implications regarding artificial intelligence (AI)-aided mammography...